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Record W2908662426 · doi:10.47339/ephj.2018.67

General radon gas knowledge test assessment for BCIT students

2018· article· en· W2908662426 on OpenAlexvenueaboutno aff
Jamie Zhang, Environmental Health BCIT School of Health Sciences, Helen Heacock, Jeffrey Ma

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2018
Typearticle
Languageen
FieldHealth Professions
TopicRadioactivity and Radon Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsRadonHealth hazardRadon gasTest (biology)Significant differenceEnvironmental healthGeographyHazardPsychologyMedicineGeologyMathematicsStatisticsPhysics

Abstract

fetched live from OpenAlex

Background Vancouver is located in a generally low-radon hazard zone. However, other parts of British Columbia such as the BC Interior or Northern BC are classified as high-radon hazard zone (or zone 1) due to the geological composition of rocks and soils in those areas. Despite the significant health risks associated with radon gas exposure, many BC residents and people across Canada have little to no knowledge regarding the topic. Since Post-secondary schools, such as the British Columbia Institute of Technology (BCIT), are places where knowledge is distributed and shared to our societies, it is important to assess students’ general knowledge background regarding radon gas. The result can then be extrapolated to the general populations. Methods An electronic survey was conducted to determine whether students in the six schools at BCIT have different background knowledge level regarding radon gas. The survey also determines students’ radon background knowledge based on different geographic regions they reside. The survey was conducted in-person at three main locations across BCIT’s Burnaby campus. It was administered using Google Forms and distributed to participants on Microsoft Surface 2. Results The One-way ANOVA statistical analysis result indicated that there is a significant difference in mean radon survey scores among the six various BCIT schools(p=0.009). In addition, the Tukey Test revealed that students from the School of Health Science have an average radon survey score which is significantly different when compared to students from the School of Business. However, it was found that there is no significant difference in the mean radon survey scores between the School of Business and other schools at BCIT. Nonetheless, it was evident that the School of Health Science students had relatively higher radon survey scores and thus, were more knowledgeable regarding radon gas compared to students from the other five schools. When analyzing survey scores among students residing in various geographic regions, the test showed that there is no significant difference in mean radon survey scores among BCIT students living in various geographic locations(p=0.46). Conclusion Based on the result of the study, the result showed that there is a significant difference in radon gas knowledge among BCIT students who majored in different schools. The School of Health Science students were more knowledgeable regarding the topic of radon gas compared to students in other schools. Nonetheless, all BCIT students achieved an average radon survey score of less than five out of ten, which was considered a failure score (Less than five out of ten). This showed that most BCIT students had very limited knowledge regarding radon gas and there were very limited amount of educational initiatives or campaigns available for students at BCIT. BCIT’s student association is recommended to create educational sessions across campus to raise student awareness regarding radon gas. At the community level, governments and various agencies such as the BC Lung Association need to work together to create radon awareness campaigns across BC and the rest of Canada. In order to get a more accurate representation of the radon gas knowledge level among people in BC, more research studies need to be conducted in other schools or general population groups.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.146
GPT teacher head0.473
Teacher spread0.328 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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